CRATE: Accurate and efficient clustering-based nonlinear analysis of heterogeneous materials through computational homogenization
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Updated
Nov 15, 2023 - Python
CRATE: Accurate and efficient clustering-based nonlinear analysis of heterogeneous materials through computational homogenization
The Clusters-Features package allows data science users to compute high-level linear algebra operations on any type of data set. It computes approximatively 40 internal evaluation scores such as Davies-Bouldin Index, C Index, Dunn and its Generalized Indexes and many more ! Other features are also available to evaluate the clustering quality.
Code used to identify and analyze drought clusters from gridded data.
Optimize clustering labels using Silhouette Score.
Clustering and resource allocation using Deterministic Annealing Approach and Orthogonal Non-negative Matrix Factorization O-(NMF)
Cluster Validity Index Using a Distance-based Separability Measure
Defines a boundary around cluster centers in a given point-layer shapefile.
Random Neighbors: Random Forest style clustering for high-dimensional data
code for PhD thesis
Mutual Information-based Non-linear Clustering Analysis
Cheminformatics based project that aims to assess the diversity of the known inhibitors of SarsCov-2 proteases taken from COVID Moonshot project.
A web app to detect sites' vulnerabilities and prioritize findings that need to be addressed soon
A concatenation of two GNNs to decode dynamic clustering on localization datasets
E-commerce Data Pipeline
Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.
Clustering - Cohort Analysis - Retention Analysis
Library of popular algorithms implemented in a parallel way
Selection of the best centroid based clustering version with k-medoids and k-means
Implemented an auto-clustering tool with seed and number of clusters finder. Optimizing algorithms: Silhouette, Elbow. Clustering algorithms: k-Means, Bisecting k-Means, Gaussian Mixture. Module includes micro-macro pivoting, and dashboards displaying radius, centroids, and inertia of clusters. Used: Python, Pyspark, Matplotlib, Spark MLlib.
An application for clustering keywords in polish based on text morphology or semantic connections.
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